General Information Edit

Introduces statistical signal processing. Signal representation and manipulation are covered via correlation and using the DFT/FFT to estimate other transforms; applications of these topics are then covered, including quantization, quantization effects in digital filters, multirate DSP, filter banks, delta-sigma modulation, power spectrum estimation, and introductions to Wiener and Kalman filtering and image processing.

Prerequisites Edit

Prerequisite:  ECE 3250

Outcomes Edit

  • Be able to draw Fourier spectra in both discrete-time frequency and continuous-time frequency, while undergoing common operations such as filtering, upsampling, downsampling, A/D and D/A.
  • Given a finite-duration, discrete-time signal, be able to estimate the discrete-time Fourier Transform and the original frequency content in the signal (e.g., the continuous-time Fourier Transform) and give bounds on the accuracy of the estimate.
  • Given a continuous-time signal with certain frequency- and time-based characteristics, be able to design a (real-world, non-ideal) system to appropriately sample the signal such that desired characteristics are maintained to within given tolerances.
  • Given a digital audio (1-d) or image (2-d) signal, be able to select an appropriate frequency- based transform suitable for the desired or specified processing.
  • Quickly prototype and debug signal processing algorithms using Matlab.

Workload Edit

SP17 - Light. 2 problem sets. 2 exams. 1 project (+report+presentation).

Advice Edit

SP17- Be careful not to fall behind on material as there is virtually no formal work for this class.

The past 4 offerings of the course (SP15, 16, 17, 18) have all had different instructors, so be warned that the class is changing.

Past Offerings Edit

Semester Time Professor Median Grade
Spring 2017

TR 11:40-12:55

David Delchamps A
Spring 2018

TR 11:40-12:55

Mert Sabuncu
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